3 code implementations • 19 Jan 2024 • Lihe Yang, Bingyi Kang, Zilong Huang, Xiaogang Xu, Jiashi Feng, Hengshuang Zhao
To this end, we scale up the dataset by designing a data engine to collect and automatically annotate large-scale unlabeled data (~62M), which significantly enlarges the data coverage and thus is able to reduce the generalization error.
Ranked #3 on Monocular Depth Estimation on NYU-Depth V2 (using extra training data)
no code implementations • 30 Nov 2023 • Yau Shing Jonathan Cheung, Xi Chen, Lihe Yang, Hengshuang Zhao
We thus propose a lightweight clustering framework for unsupervised semantic segmentation.
1 code implementation • NeurIPS 2023 • Lihe Yang, Xiaogang Xu, Bingyi Kang, Yinghuan Shi, Hengshuang Zhao
Then, we investigate the role of synthetic images by joint training with real images, or pre-training for real images.
1 code implementation • ICCV 2023 • Yijiang Li, Xinjiang Wang, Lihe Yang, Litong Feng, Wayne Zhang, Ying Gao
Deep co-training has been introduced to semi-supervised segmentation and achieves impressive results, yet few studies have explored the working mechanism behind it.
1 code implementation • ICCV 2023 • Lihe Yang, Zhen Zhao, Lei Qi, Yu Qiao, Yinghuan Shi, Hengshuang Zhao
To mitigate potentially incorrect pseudo labels, recent frameworks mostly set a fixed confidence threshold to discard uncertain samples.
1 code implementation • CVPR 2023 • Zhen Zhao, Lihe Yang, Sifan Long, Jimin Pi, Luping Zhou, Jingdong Wang
Differently, in this work, we follow a standard teacher-student framework and propose AugSeg, a simple and clean approach that focuses mainly on data perturbations to boost the SSS performance.
1 code implementation • CVPR 2023 • Lihe Yang, Lei Qi, Litong Feng, Wayne Zhang, Yinghuan Shi
In this work, we revisit the weak-to-strong consistency framework, popularized by FixMatch from semi-supervised classification, where the prediction of a weakly perturbed image serves as supervision for its strongly perturbed version.
Ranked #1 on Semi-supervised Change Detection on WHU - 10% labeled data (IoU metric)
Semi-supervised Change Detection Semi-supervised Medical Image Segmentation +1
1 code implementation • CVPR 2022 • Lihe Yang, Wei Zhuo, Lei Qi, Yinghuan Shi, Yang Gao
In this work, we first construct a strong baseline of self-training (namely ST) for semi-supervised semantic segmentation via injecting strong data augmentations (SDA) on unlabeled images to alleviate overfitting noisy labels as well as decouple similar predictions between the teacher and student.
1 code implementation • ICCV 2021 • Lihe Yang, Wei Zhuo, Lei Qi, Yinghuan Shi, Yang Gao
Our method aims to alleviate this problem and enhance the feature embedding on latent novel classes.
Ranked #41 on Few-Shot Semantic Segmentation on PASCAL-5i (5-Shot)